We further validate the performance of EfficientFormer on the semantic segmentation task.
We use the challenging scene parsing dataset, ADE20K [1]}, [2]}, which contains 20K training images and 2K validation ones covering 150 class categories.
Similar to existing work [3]}, we build EfficientFormer as backbone along with Semantic FPN [4]} as segmentation decoder for fair comparison. The backbone is initialized with
pretrained weights on ImageNet-1K and the model is trained for 40K iterations with a total batch size of 32 over 8 GPUs.
We follow the common practice in segmentation [3]}, [6]}, use AdamW optimizer [7]}, [8]}, and apply a poly learning rate schedule with power \(0.9\) , starting from a initial learning rate \(2\times 10^{-4}\) .
We resize and crop input images to \(512\times 512\)  for training and shorter side as 512 for testing (on validation set).
